My Mobile Music: An Adaptive Personalization System For Digital Audio Players

This paper develops a music recommendation system that automates the downloading of songs into a mobile digital audio device. The system tailors the composition of the songs to the preferences of individuals based on past behaviors. By assuming that an individual will listen longer to a song that provides a higher utility, we describe and predict individual listening behavior using a lognormal hazard function. Our recommendation system is the first to accomplish this and there is no viable alternative. Yet, our proposed approach provides an improvement over naïve methods that could be used for product recommendations. Our system has a number of distinct features. First, we use of a Sequential Monte Carlo algorithm that enables the system to deal with massive historic datasets on listening behavior of individuals. Second, we apply a variable selection procedure that helps to reduce the dimensionality of the problem, because in many applications the collection of songs need to be described by a very large number of explanatory variables (in particular music genres variables). Third, our system recommends a batch of products rather than a single product, taking into account the predicted utility and the uncertainty in the parameter estimates, and applying experimental design methods. The simulation section of this paper demonstrated that our model does achieve it objectives in handling massive data and improving predictions through model averaging. By using simulated data in the simulation, and thus knowing the true parameters, the Sequential Monte Carlo and variable selection procedures were shown to provide good estimates of an individual's preferences. Experimental results show that variable selection does simplify estimation and prediction as different individuals differ in the number of variables need to definite their listening behaviors. The results also show that for some individuals, model averaging does in fact help to improve predictions. The results of the experiment show that our model provides 23 - 35% improvement in recommendations. This improvement is achieved in a single wave and in a natural experimental setting in which the subjects have a choice or when, where and how they want to listen to the songs

Single Versus Multiple Source Purchasing Strategy

In this paper, we look at the choice between a single and a multiple source purchasing strategy. Using a game theoretic approach, we examine the impact of the economies of scale and specific knowledge on the choice of sourcing strategy, explicitly taking into account the small numbers interactions involving a buyer and two competing suppliers. We show that economies of scale and specific knowledge have opposing effects on sourcing strategies. While a single source strategy is favored when efficiency gains due to economies of scale are large, a multiple source strategy is the dominant strategy in the long run when specific knowledge acquired by a supplier becomes substantial. In following a multiple source strategy, it is also optimal for a buyer to split the supply contract symmetrically across the suppliers, in order to appropriate all efficiency gains that result from the acquisition of specific knowledge by its suppliers. However, splitting of the supply contract results in a reduction in gains due to the economies of scale

Single versus multiple source purchasing strategy

Research Paper Series (National University of Singapore. Faculty of Business Administration); 1999-0121-2

My Mobile Music: An Adaptive Personalization System for Digital Audio Players

New information technologies increasingly make it possible for service providers to adaptively personalize their service, fine-tuning the service over time for each individual customer, based on observation of that customer's behavior. We propose an “Adaptive Personalization System” and illustrate its implementation for digital audio players, a product category with rapidly expanding sales. The proposed system automatically downloads personalized playlists of MP3 songs into a consumer's mobile digital audio device and requires little proactive user effort (i.e., no explicit indication of preferences or ratings for songs). The system works in real time and is scalable to the massive data typically encountered in personalization applications. A simulation study shows the Adaptive Personalization System to outperform benchmark approaches. We implemented the Adaptive Personalization System on Palm PDAs and tested its performance with digital audio users. For actual users, the Adaptive Personalization System provides substantial improvements over benchmark approaches both in terms of the number of songs listened to and listening duration.digital audio players, service marketing, personalization, customization, collaborative filtering, one-to-one marketing

Marketing Models of Service and Relationships

Given the growth of the service sector, and advances in information technology and communications that facilitate the management of relationships with customers, models of service and relationships are a fast-growing area of marketing science. This article summarizes existing work in this area and identifies promising topics for future research. Models of service and relationships can help managers manage service more efficiently, customize service more effectively, manage customer satisfaction and relationships, and model the financial impact of those customer relationships. Models for managing service have often emphasized analytical approaches to pricing, but emerging issues such as the trade-off between privacy and customization are attracting increasing attention. The trade-offs between productivity and customization have also been addressed by both analytical and empirical models, but future research in the area of service customization will likely place increased emphasis on e-service and truly personalized interactions. Relationship models will focus less on models of customer expectations and length of relationship, and more on modeling the effects of dynamic marketing interventions with individual customers. The nature of service relationships increasingly leads to financial impact being assessed within customer and across product, rather than the traditional reverse, suggesting the increasing importance of analyzing customer lifetime value (CLV) and managing the firm's customer equity.services marketing, relationship marketing, customer satisfaction, service quality, service productivity, customization, service design, e-service, service demand, pricing of services, service guarantees, complaint management, customer retention, customer relationship management, word of mouth, customer lifetime value, customer equity, return on quality